// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
// Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;

using Eigen::RowMajor;
using Eigen::Tensor;
template<typename DataType, int DataLayout, typename IndexType>
static void
test_tanh_sycl(const Eigen::SyclDevice& sycl_device)
{

	IndexType sizeDim1 = 4;
	IndexType sizeDim2 = 4;
	IndexType sizeDim3 = 1;
	array<IndexType, 3> tensorRange = { { sizeDim1, sizeDim2, sizeDim3 } };
	Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
	Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
	Tensor<DataType, 3, DataLayout, IndexType> out_cpu(tensorRange);

	in = in.random();

	DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
	DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));

	TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
	TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);

	sycl_device.memcpyHostToDevice(gpu_data1, in.data(), (in.size()) * sizeof(DataType));
	gpu2.device(sycl_device) = gpu1.tanh();
	sycl_device.memcpyDeviceToHost(out.data(), gpu_data2, (out.size()) * sizeof(DataType));

	out_cpu = in.tanh();

	for (int i = 0; i < in.size(); ++i) {
		VERIFY_IS_APPROX(out(i), out_cpu(i));
	}
}
template<typename DataType, int DataLayout, typename IndexType>
static void
test_sigmoid_sycl(const Eigen::SyclDevice& sycl_device)
{

	IndexType sizeDim1 = 4;
	IndexType sizeDim2 = 4;
	IndexType sizeDim3 = 1;
	array<IndexType, 3> tensorRange = { { sizeDim1, sizeDim2, sizeDim3 } };
	Tensor<DataType, 3, DataLayout, IndexType> in(tensorRange);
	Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
	Tensor<DataType, 3, DataLayout, IndexType> out_cpu(tensorRange);

	in = in.random();

	DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in.size() * sizeof(DataType)));
	DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out.size() * sizeof(DataType)));

	TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
	TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);

	sycl_device.memcpyHostToDevice(gpu_data1, in.data(), (in.size()) * sizeof(DataType));
	gpu2.device(sycl_device) = gpu1.sigmoid();
	sycl_device.memcpyDeviceToHost(out.data(), gpu_data2, (out.size()) * sizeof(DataType));

	out_cpu = in.sigmoid();

	for (int i = 0; i < in.size(); ++i) {
		VERIFY_IS_APPROX(out(i), out_cpu(i));
	}
}

template<typename DataType, typename dev_Selector>
void
sycl_computing_test_per_device(dev_Selector s)
{
	QueueInterface queueInterface(s);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_tanh_sycl<DataType, RowMajor, int64_t>(sycl_device);
	test_tanh_sycl<DataType, ColMajor, int64_t>(sycl_device);
	test_sigmoid_sycl<DataType, RowMajor, int64_t>(sycl_device);
	test_sigmoid_sycl<DataType, ColMajor, int64_t>(sycl_device);
}

EIGEN_DECLARE_TEST(cxx11_tensor_math_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
	}
}
